research
∙
08/31/2023
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
In this article, the optimal sample complexity of learning the underlyin...
share
research
∙
08/15/2023
Graph-Structured Kernel Design for Power Flow Learning using Gaussian Processes
This paper presents a physics-inspired graph-structured kernel designed ...
research
∙
02/16/2023
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (C...
research
∙
08/30/2022
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
The alternating current (AC) chance-constrained optimal power flow (CC-O...
research
∙
07/21/2022
Data-Driven Stochastic AC-OPF using Gaussian Processes
In recent years, electricity generation has been responsible for more th...
research
∙
06/22/2022
Learning Distribution Grid Topologies: A Tutorial
Unveiling feeder topologies from data is of paramount importance to adva...
research
∙
01/06/2022
Detecting Anomalies using Overlapping Electrical Measurements in Smart Power Grids
As cyber-attacks against critical infrastructure become more frequent, i...
research
∙
12/04/2021
DNN-based Policies for Stochastic AC OPF
A prominent challenge to the safe and optimal operation of the modern po...
research
∙
11/23/2021
Importance sampling approach to chance-constrained DC optimal power flow
Despite significant economic and ecological effects, a higher level of r...
research
∙
10/02/2021
Learning Networked Linear Dynamical Systems under Non-white Excitation from a Single Trajectory
We consider a networked linear dynamical system with p agents/nodes. We ...
research
∙
07/05/2021
Physics-Informed Graph Learning for Robust Fault Location in Distribution Systems
The rapid growth of distributed energy resources potentially increases p...
research
∙
09/26/2020
Estimating Linear Dynamical Networks of Cyclostationary Processes
Topology learning is an important problem in dynamical systems with impl...
research
∙
05/05/2020
A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications
Due to proliferation of energy efficiency measures and availability of t...
research
∙
05/04/2020
Tractable learning in under-excited power grids
Estimating the structure of physical flow networks such as power grids i...
research
∙
04/20/2020
Data-Driven Learning and Load Ensemble Control
Demand response (DR) programs aim to engage distributed small-scale flex...
research
∙
11/14/2019
Real-time Anomaly Detection and Classification in Streaming PMU Data
Ensuring secure and reliable operations of the power grid is a primary c...
research
∙
01/03/2019
Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models f...
research
∙
10/11/2018
Real-time Fault Localization in Power Grids With Convolutional Neural Networks
Diverse fault types, fast re-closures and complicated transient states a...
research
∙
09/27/2018
Physics Informed Topology Learning in Networks of Linear Dynamical Systems
Learning influence pathways of a network of dynamically related processe...
research
∙
03/17/2018
Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids
Distribution grid is the medium and low voltage part of a large power sy...
research
∙
10/27/2017
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of tr...
research
∙
07/05/2017
